Title of Presentation: The Applicability of a Sensor Web Simulator to Evaluate a Future Lidar Mission

Primary (Corresponding) Author: Michael Seablom

Organization of Primary Author: NASA Goddard Space Flight Center


Abstract: Under a recently-funded ESTO award  we are now designing, and will eventually build, a sensor web architecture that couples future Earth observing systems with atmospheric, chemical, and oceanographic models and data assimilation systems.  The end product will be a "sensor web simulator" (SWS) based upon the proposed architecture that would objectively quantify the scientific return of a fully functional model-driven meteorological sensor web. Our proposed work is based upon two ESTO-funded studies that have yielded a sensor web-based 2025 weather observing system architecture, and a preliminary SWS software architecture funded by RASC and other technology awards. Sensor Web observing systems have the potential to significantly improve our ability to monitor, understand, and predict the evolution of rapidly evolving, transient, or variable meteorological features and events. A revolutionary architectural characteristic that could substantially reduce meteorological forecast uncertainty is the use of targeted observations guided by advanced analytical techniques (e.g., prediction of ensemble variance). Simulation is essential: investing in the design and implementation of such a complex observing system would be very costly and almost certainly involve significant risk. A SWS would provide information systems engineers and Earth scientists with the ability to define and model candidate designs, and to quantitatively measure predictive forecast skill improvements. The SWS will serve as a necessary trade studies tool to: evaluate the impact of selecting different types and quantities of remote sensing and in situ sensors; characterize alternative platform vantage points and measurement modes; and to explore rules of interaction between sensors and with weather forecast/data assimilation components to reduce model error growth and forecast uncertainty. We will demonstrate key SWS elements using a proposed future lidar mission as a use case.